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Hawkes processes are temporal self-exciting point processes. They are well established in earthquake modelling or finance and their application is spreading to diverse areas. Most models from the literature have two major drawbacks regarding their potential application to insurance. First, they use an exponentially-decaying form of excitation, which does not allow a delay between the occurrence of an event and its excitation effect on the process and does not fit well on insurance data consequently. Second, theoretical results developed from these models are valid only when time of observation tends to infinity, whereas the time horizon for an insurance use case is of several months or years. In this paper, we define a complete framework of Hawkes processes with a Gamma density excitation function (i.e. estimation, simulation, goodness-of-fit) instead of an exponential-decaying function and we demonstrate some mathematical properties (i.e. expectation, variance) about the transient regime of the process. We illustrate our results with real insurance data about natural disasters in Luxembourg. [ABSTRACT FROM AUTHOR]

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease. [ABSTRACT FROM AUTHOR]

We construct a recommendation system for car insurance, to allow agents to optimize up-selling performances, by selecting customers who are most likely to subscribe an additional cover. The originality of our recommendation system is to be suited for the insurance context. While traditional recommendation systems, designed for online platforms (e.g. e-commerce, videos), are constructed on huge datasets and aim to suggest the next best offer, insurance products have specific properties which imply that we must adopt a different approach. Our recommendation system combines the XGBoost algorithm and the Apriori algorithm to choose which customer should be recommended and which cover to recommend, respectively. It has been tested in a pilot phase of around 150 recommendations, which shows that the approach outperforms standard results for similar up-selling campaigns. [ABSTRACT FROM AUTHOR]

Cyber security is a critical area in computer systems especially when dealing with sensitive data. At present, it is becoming increasingly important to assure that computer systems are secured from attacks due to modern society dependence from those systems. To prevent these attacks, nowadays most organizations make use of anomaly-based intrusion detection systems (IDS). Usually, IDS contain machine learning algorithms which aid in predicting or detecting anomalous patterns in computer systems. Most of these algorithms are supervised techniques, which contain gaps in the detection of unknown patterns or zero-day exploits, since these are not present in the algorithm learning phase. To address this problem, we present in this paper an empirical study of several unsupervised learning algorithms used in the detection of unknown attacks. In this study we evaluated and compared the performance of different types of anomaly detection techniques in two public available datasets: the NSL-KDD and the ISCX. The aim of this evaluation allows us to understand the behavior of these techniques and understand how they could be fitted in an IDS to fill the mentioned flaw. Also, the present evaluation could be used in the future, as a comparison of results with other unsupervised algorithms applied in the cybersecurity field. The results obtained show that the techniques used are capable of carrying out anomaly detection with an acceptable performance and thus making them suitable candidates for future integration in intrusion detection tools. [ABSTRACT FROM AUTHOR]

In January 2013, a 54-year-old pseudophakic woman was referred to the ocular inflammation clinic with a diagnosis of panuveitis, already on oral cyclosporine (100 mg/day) since 2010. She was a monocular patient, with the left eye eviscerated due to trauma and secondary endophthalmitis in 1995. She had experienced similar recurring episodes between 2010 and 2013. Examination revealed best-corrected visual acuity (BCVA) of 20/200. The anterior segment revealed the presence of flare and absence of cells. Intraocular pressure (IOP) was 11 mmHg. Fundus examination revealed the presence of anterior vitreous organization, exuberant cystoid macular edema (CME) and peripheral Dalen-Fuchs nodules with no activity. Systemic workup showed no changes, and a presumptive diagnosis of sympathetic ophthalmia was made. Initial therapy included topical and systemic corticosteroids. Cyclosporine was replaced with methotrexate (20 mg/week) due to nephrotoxicity. There was marked improvement in anterior inflammation and vitreous organization, but the ME remained exuberant (central ring thickness of 0.7 mm). Over the subsequent 4 years of follow-up, the patient received two intravitreous injections of a dexamethasone extended-release implant (Ozurdex®), with normalization of macular thickness and improvement in visual acuity (VA), but ME recurred around 6 months post-injection. During this period, the patient also underwent three sub-tenon injections of triamcinolone, with marked improvement of ME, but ME recurred between 4 and 6 months after treatment. She was thus recommended for treatment with a fluocinolone extended-release implant (ILUVIEN®). Post-implantation (3-year follow-up), she had controlled anterior inflammation without ME (central macular thickness of 248 µm). This case suggests that sustained control of inflammation enables better control of ME, and benefits may persist even after cessation of the direct anti-inflammatory effect of the implant. Funding: Alimera Sciences Ltd funded the Rapid Service Fees. [ABSTRACT FROM AUTHOR]